Assessing Knowledge Quality Using Fuzzy MCDM Model

Chiu-Chi Wei (), Chih-Chien Tai, Shun-Chin Lee and Meng-Ling Chang
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Chiu-Chi Wei: Ph.D. Program of Management, Chung Hua University, Hsinchu City 30012, Taiwan
Chih-Chien Tai: Ph.D. Program of Management, Chung Hua University, Hsinchu City 30012, Taiwan
Shun-Chin Lee: Ph.D. Program of Management, Chung Hua University, Hsinchu City 30012, Taiwan
Meng-Ling Chang: Ph.D. Program of Management, Chung Hua University, Hsinchu City 30012, Taiwan

Mathematics, 2023, vol. 11, issue 17, 1-16

Abstract: The purpose of knowledge management is to excavate the tacit knowledge accumulated by each enterprise member through the knowledge proposal system. Each knowledge proposal must be assessed, and after passing the quality assessment, the knowledge proposal will be stored in the knowledge repository and shared with other employees who need the knowledge at work. In the long run, the capabilities of all employees will gradually enhance and the competitiveness of enterprises will naturally increase. The correct assessment of knowledge quality is the key to the success of knowledge management. Some scholars propose to use the AHP (analytical hierarchical process) to determine the quality of knowledge. The problem with this approach is that the AHP can only obtain the relative quality of all knowledge, not the actual quality of knowledge. Therefore, this study proposes a fuzzy assessment model to measure knowledge quality, which includes a knowledge quality fuzziness index (KQFI) and a checking gate. First, experts conduct linguistic evaluation on the weight of criteria and knowledge quality. All linguistic evaluations are then integrated into a knowledge quality fuzziness index (KQFI), which is compared with a fuzzy threshold (FT); then, the level of goodness of KQFI to FT is obtained. If it is greater than 0.5, it means that the quality of the knowledge proposal is qualified; otherwise, it means that the quality of the knowledge proposal is unqualified. This study uses a case including five experts and nine knowledge proposals to demonstrate the applicability of the method. The results show that the method finally judges six knowledge instances as qualified and three as unqualified. The results show that the proposed method can indeed assist enterprises to effectively screen knowledge proposals.

Keywords: knowledge management; fuzzy theory; multi-criteria decision making (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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